Abstract: As Internet commerce becomes more popular, customers' preferences on various products can now be readily acquired on-line via various e-commerce systems. Properly mining this extracted data can generate useful knowledge for providing personalized product recommendation services. In general, recommender systems use two complementary techniques. Content-based systems match customer interests with products attributes, while collaborative filtering systems utilize preference ratings from other customers. In this paper, we address some problems faced by these two systems, and study how machine learning techniques, namely the support vector machine and the latent class model, can be used to alleviated them.
Proceedings of the Second International Conference on Data Mining Methods and Databases for Engineering, pp.601-610, Cambridge, UK, July 2000.
Postscript: http://www.cs.ust.hk/~jamesk/papers/dm00.ps.gz